Lately, I have been having conversations with students about uneasiness in doing graduate work in mathematics. They are not necessarily anxious about the math; however, it is what they will do after. As a graduate student in mathematics, it seems teaching is the only direction you can take (this is my direction of choice). There are other options though. One such option is data scientist.

Demand for data professionals is far outpacing supply, and that should continue as more companies look to analyze and benefit from the data they’re collecting.” Michael Koploy, Software Advice

Koploy sent me an email earlier this week about an article he has written entitled “3 Career Secrets for Aspiring Data Scientists.” My first question when reading his email was, what is a data scientist exactly. Koploy quotes Adlucent CTO Michael Griffin as saying,

Data scientist is just the new-age term applied to people who have worked in statistics, machine learning or artificial learning in the past,” says Griffin. “It’s a new moniker to apply to some of the same people.

Koploy says that

Indeed.com confirms: data scientist is one of the fastest growing job postings in its database.

So the question is, how exactly does this apply to students in mathematics graduate studies? Koploy’s three career secrets are

**Sharpen Your Scientific Saw**– The article quotes Global Analytics Holdings COO Krishna Gopinathan as saying,

The solution to a problem may be hidden in a particular machine learning algorithm or a traditional statistical model…Individuals experienced in various domains and working with different problems will be the ones who succeed.

Honestly, what mathematics graduate student does not have experience in all of this? Mathematics is all about performing and developing algorithms for a precise and logistical way of solving a problem. Mathematicians relate these algorithms to new problems and adjust them to solve different problems. This is exactly what Gopinathan is asking for in a data scientist.

**Learn the Language of Business**– This part would require some additional work from the student. Griffin says,

Working in a commercial environment is just different from academia.

Koploy adds,

For example, Griffin notes that unlike in most academic environments, his position at Adlucent affords a team of developers to assist as needed. To succeed, individuals must be able to delegate tasks, manage projects, and lead teams–in addition to wizardly manipulating data.

Mathematicians clearly understand how to manipulate data. In fact, they are some of the best at doing it; however, it can sometimes be hard for us to delegate tasks. At this point in our academic careers, we are thinking like academics (not a bad thing); however, for working in industry you may have to alter that mind-set a bit. Read more about what Koploy has to say about this tip in his article.

**Keep Adding to Your Technical Toolbelt**– Many graduate students encounter the necessity of working with software in their studies. Whether it be excel, R, SPSS, SAS, Mathematica, MATLAB, Octave, etc., we have all done some of it. Gopinathan says,

Becoming an effective data scientist is all about playing with the data cards you’re dealt. The more tools you’ve mastered, the stronger your play.

In fact, Koploy mentions Excel, SPSS, SAS, and R as being beneficial softwares to understand. Mathematicians already have an advantage in this area because we are designed to think in algorithmic manners. Now all you have to do is learn the syntax. That is the easy part.

Have you ever considered data scientist as a career for you? What have you heard about data scientists? Which of these three tips do you think would be the most difficult for you to achieve?